标题： 监控视频的特征图间差分编码 显示英文标题

标题： Inter-Feature-Map Differential Coding of Surveillance Video

摘要： 在协作智能中，深度神经网络（DNN）被分割并在边缘和云上部署，以节省带宽并优化系统。 当模型输入是图像时，已确认从边缘输出的中间特征图的大小可以小于输入数据的大小。 然而，当输入是视频时，其效果尚未有报道。 在本研究中，我们提出了一种方法，通过应用特征图间差分编码（IFMDC）来压缩监控视频的特征图。 IFMDC在精度小幅降低的情况下，对输入视频的压缩比与HEVC相当或更好。 我们的方法在HEVC应用时对图像质量退化敏感的视频特别有效。 显示英文摘要

摘要： In Collaborative Intelligence, a deep neural network (DNN) is partitioned and deployed at the edge and the cloud for bandwidth saving and system optimization. When a model input is an image, it has been confirmed that the intermediate feature map, the output from the edge, can be smaller than the input data size. However, its effectiveness has not been reported when the input is a video. In this study, we propose a method to compress the feature map of surveillance videos by applying inter-feature-map differential coding (IFMDC). IFMDC shows a compression ratio comparable to, or better than, HEVC to the input video in the case of small accuracy reduction. Our method is especially effective for videos that are sensitive to image quality degradation when HEVC is applied